{"id":2216589,"url":"https://alion.io/job/testtriangle-data-scientist-lead","title":"Data scientist lead","company":{"id":4380438,"name":"Test Triangle","domain":"testtriangle.com","url":"https://alion.io/company/testtriangle","size_band":"1001-5000","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Leeds, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":92000,"max_usd":163000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Neptune","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"ArangoDB","optional":false},{"name":"Azure","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"CI/CD","optional":false},{"name":"CVE","optional":false},{"name":"CWE","optional":false},{"name":"Databricks","optional":false},{"name":"DGL","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"GNN","optional":false},{"name":"GraphRAG","optional":false},{"name":"IAM","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"MITRE ATT&CK","optional":false},{"name":"Neo4j","optional":false},{"name":"Python","optional":false},{"name":"SIEM","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"STIX","optional":false},{"name":"TAXII","optional":false},{"name":"Zero Trust","optional":false}],"status":"live","first_seen_at":"2026-10-10T08:37:28Z","employer_posted_date":"2026-10-10","last_verified_at":"2026-10-11T20:45:06Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"LEAD DATA SCIENTIST\nSecurity Knowledge Graphs & Cyber AI\nRole Level\nLead \nExperience\n10+ years overall; 5+ years in AI/ML or graph analytics\nLocation\nFlexible / Hybrid\nEmployment Type\nFull-time\nRole Purpose\nLead the design, engineering, and operationalization of enterprise Security Knowledge Graphs that connect security telemetry, assets, identities, vulnerabilities, threats, controls, and incidents into a trusted intelligence layer. The role is highly hands-on and combines data science, graph engineering, cybersecurity analytics, semantic modeling, and technical leadership to enable attack-path analysis, threat investigation, exposure prioritization, GraphRAG, and AI-assisted security operations.\nKey Responsibilities\nDesign the Security Knowledge Graph architecture, ontology, taxonomy, entity model, relationship model, provenance model, and lifecycle standards.\nBuild production-grade graph ingestion and transformation pipelines for SIEM, EDR/XDR, IAM/PAM, CMDB, vulnerability scanners, cloud security platforms, threat intelligence feeds, security data lakes, and case-management systems.\nDevelop entity extraction, identity resolution, deduplication, schema mapping, relationship inference, confidence scoring, temporal modeling, and graph enrichment capabilities.\nModel assets, applications, users, service accounts, privileges, vulnerabilities, misconfigurations, controls, alerts, incidents, indicators, threat actors, campaigns, tactics, techniques, and procedures.\nImplement graph analytics for attack paths, blast radius, privilege escalation, lateral movement, toxic combinations, identity exposure, control gaps, and vulnerability prioritization.\nBuild and evaluate graph algorithms and ML models including centrality, community detection, similarity, anomaly detection, node classification, link prediction, embeddings, and Graph Neural Networks.\nDesign GraphRAGand knowledge-grounded security assistants that combine graph traversal, vector retrieval, structured evidence, LLM reasoning, citations, and human approval controls.\nPartner with SOC, threat intelligence, IAM, vulnerability management, cloud security, architecture, data engineering, and product teams to convert operational problems into reusable graph-powered capabilities.\nOwn technical design reviews, coding standards, model validation, observability, performance tuning, security controls, documentation, and production-readiness gates.\nMentor data scientists and engineers while remainingaccountable for prototypes, reference implementations, critical code, troubleshooting, and complex customer or stakeholder demonstrations.\nMandatory Hands-on Technical Skills\nKnowledge graphs: Ontology and semantic model design; property graphs and RDF; graph schema evolution; knowledge representation; provenance; graph quality; entity and relationship resolution.\nGraph platforms: Deep implementation experience with Neo4j and Cypher; workingknowledge of at least one additionalplatform such as Amazon Neptune, TigerGraph, Azure Cosmos DB Gremlin, ArangoDB, or JanusGraph.\nGraph data science: Neo4j Graph Data Science, NetworkX, PyTorchGeometric or DGL; graph embeddings, pathfinding, similarity, clustering, link prediction, node classification, anomaly detection, and GNN development.\nProgramming and engineering: Advanced Python and SQL; APIs; test automation; data structures; distributed processing; Git; CI/CD; containers; infrastructure awareness; production debugging and performance optimization.\nData engineering: Spark or Databricks, Kafka or equivalent streaming, ETL/ELT, batch and real-time pipelines, data contracts, lineage, cataloguing, quality rules, and scalable cloud storage.\nCybersecurity: SOC workflows, threat hunting, incident response, detection engineering, vulnerability and exposure management, IAM/PAM, Zero Trust, cloud security, and security control mapping.\nSecurity standards: Practical use of MITRE ATT&CK, STIX/TAXII, CVE, CWE, CAPEC, NIST frameworks, CIS Controls, and common threat-intelligence vocabularies.\nGenAI and GraphRAG: LLM-based extraction, retrieval orchestration, agent/tool integration, prompt design, evaluation, grounding, guardrails, explainability, and evidence traceability.\nMLOpsand observability: Experiment tracking, model versioning, deployment, monitoring, drift and quality checks, auditability, access controls, secretsmanagement, and cost/performance management.\nSecurity Knowledge Graph Engineering Expectations\nCreate canonicalentity and relationship definitions with stable identifiers, temporal context, source lineage, evidence attributes, confidence scores, and access-control classifications.\nDevelop reusable connectors and parsers for structured, semi-structured, and unstructured security sour","description_format":"text","description_chars":4753,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United Kingdom","iso":"GB","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Information Security"],"lifecycle":[{"event":"open","at":"2026-10-10T08:37:28Z"}],"visa":[],"liveness":{"score":52,"band":"ok","label":"Likely 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